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Course Outline
Statistics & Probabilistic Programming in Julia
Basic Statistics
-
Statistics
- Summary Statistics using the statistics package
-
Distributions & StatsBase package
- Univariate & Multivariate distributions
- Moments
- Probability functions
- Sampling and RNG
- Histograms
- Maximum likelihood estimation
- Product, truncation, and censored distributions
- Robust statistics
- Correlation & covariance
DataFrames
(DataFrames package)
- Data Input/Output
- Creating DataFrames
- Data types, including categorical and missing data
- Sorting & Joining
- Reshaping & Pivoting data
Hypothesis Testing
(HypothesisTests package)
- Overview of hypothesis testing principles
- Chi-Squared test
- z-test and t-test
- F-test
- Fisher's exact test
- ANOVA
- Normality tests
- Kolmogorov-Smirnov test
- Hotelling's T-test
Regression & Survival Analysis
(GLM & Survival packages)
- Overview of linear regression and the exponential family
- Linear regression
-
Generalized Linear Models
- Logistic regression
- Poisson regression
- Gamma regression
- Other GLM models
-
Survival Analysis
- Events
- Kaplan-Meier
- Nelson-Aalen
- Cox Proportional Hazards
Distances
(Distances package)
- Understanding distance metrics
- Euclidean distance
- Cityblock distance
- Cosine distance
- Correlation distance
- Mahalanobis distance
- Hamming distance
- MAD
- RMS
- Mean Squared Deviation
Multivariate Statistics
(MultivariateStats, Lasso, & Loess packages)
- Ridge regression
- Lasso regression
- Loess
- Linear Discriminant Analysis
-
Principal Component Analysis (PCA)
- Linear PCA
- Kernel PCA
- Probabilistic PCA
- Independent Component Analysis
- Principal Component Regression (PCR)
- Factor Analysis
- Canonical Correlation Analysis
- Multidimensional Scaling
Clustering
(Clustering package)
- K-means
- K-medoids
- DBSCAN
- Hierarchical clustering
- Markov Cluster Algorithm
- Fuzzy C-means clustering
Bayesian Statistics & Probabilistic Programming
(Turing package)
- Markov Chain Monte Carlo
- Hamiltonian Monte Carlo
- Gaussian Mixture Models
- Bayesian Linear Regression
- Bayesian Exponential Family Regression
- Bayesian Neural Networks
- Hidden Markov Models
- Particle Filtering
-
Variational Inference
Requirements
This course is intended for professionals who already have a background in data science and statistics.
21 Hours
Testimonials (4)
a multitude of points
Joanna - Instytut Ekonomiki Rolnictwa i Gospodarki Zywnosciowej-PIB
Course - Statistical Analysis with Stata and R
knowledge of the trainer, tailor based, all topics covered
eleni - EUAA
Course - Forecasting with R
The variation with exercise and showing.
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
The real life applications using Statcan and CER as examples.
